/*
 * Licensed to the Apache Software Foundation (ASF) under one
 * or more contributor license agreements.  See the NOTICE file
 * distributed with this work for additional information
 * regarding copyright ownership.  The ASF licenses this file
 * to you under the Apache License, Version 2.0 (the
 * License); you may not use this file except in compliance
 * with the License.  You may obtain a copy of the License at
 *
 *   http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing,
 * software distributed under the License is distributed on an
 * AS IS BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
 * KIND, either express or implied.  See the License for the
 * specific language governing permissions and limitations
 * under the License.
 */

/*
 * Parts of the following code in this file refs to
 * https://github.com/Tencent/ncnn/blob/master/examples/squeezenet.cpp
 * Tencent is pleased to support the open source community by making ncnn available.
 *
 * Copyright (C) 2017 THL A29 Limited, a Tencent company. All rights reserved.
 *
 * Licensed under the BSD 3-Clause License (the "License"); you may not use this
 * file except in compliance with the License. You may obtain a copy of the License at
 *
 * https://opensource.org/licenses/BSD-3-Clause
 */

/*
 * Copyright (c) 2020, OPEN AI LAB
 * Author: qtang@openailab.com
 */

#include <stdio.h>
#include <algorithm>
#include <vector>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>

// #include "platform.h"
#include "net.h"
#if NCNN_VULKAN
#include "gpu.h"
#endif // NCNN_VULKAN

static int detect_squeezenet(const cv::Mat& bgr, std::vector<float>& cls_scores)
{
    ncnn::Net squeezenet;

#if NCNN_VULKAN
    squeezenet.opt.use_vulkan_compute = true;
#endif // NCNN_VULKAN

    // the ncnn model need to be used ncnnoptimize
    squeezenet.load_param("squeezenet_v1.1.param");
    squeezenet.load_model("squeezenet_v1.1.bin");

    ncnn::Mat in = ncnn::Mat::from_pixels_resize(bgr.data, ncnn::Mat::PIXEL_BGR, bgr.cols, bgr.rows, 227, 227);

    const float mean_vals[3] = {104.f, 117.f, 123.f};
    in.substract_mean_normalize(mean_vals, 0);

    squeezenet.input("data", in);
    squeezenet.run();
    ncnn::Mat out;
    squeezenet.extract("prob", out);

    cls_scores.resize(out.total());
    for (int j=0; j<out.total(); j++)
    {
        cls_scores[j] = out[j];
    }

    return 0;
}

static int print_topk(const std::vector<float>& cls_scores, int topk)
{
    // partial sort topk with index
    int size = cls_scores.size();
    std::vector< std::pair<float, int> > vec;
    vec.resize(size);
    for (int i=0; i<size; i++)
    {
        vec[i] = std::make_pair(cls_scores[i], i);
    }

    std::partial_sort(vec.begin(), vec.begin() + topk, vec.end(),
                      std::greater< std::pair<float, int> >());

    // print topk and score
    for (int i=0; i<topk; i++)
    {
        float score = vec[i].first;
        int index = vec[i].second;
        fprintf(stderr, "%d = %f\n", index, score);
    }

    return 0;
}

int main(int argc, char** argv)
{
    if (argc != 2)
    {
        fprintf(stderr, "Usage: %s [imagepath]\n", argv[0]);
        return -1;
    }

    const char* imagepath = argv[1];

    cv::Mat m = cv::imread(imagepath, 1);
    if (m.empty())
    {
        fprintf(stderr, "cv::imread %s failed\n", imagepath);
        return -1;
    }

#if NCNN_VULKAN
    ncnn::create_gpu_instance();
#endif // NCNN_VULKAN

    std::vector<float> cls_scores;
    detect_squeezenet(m, cls_scores);

#if NCNN_VULKAN
    ncnn::destroy_gpu_instance();
#endif // NCNN_VULKAN

    print_topk(cls_scores, 3);

    return 0;
}
